1. Field of the Invention
The present invention relates to a modeling method of a software system and, more particularly, to a modeling method of a neuro-fuzzy system.
2. Description of the Related Art
Neuro-fuzzy system is presently used for system modeling or training. Conventionally, the way to construct a neuro-fuzzy system is defining fuzzy rules according to training data and refining the fuzzy rules to obtain an output function set through function approximation. Specifically, the obtained output function set is capable of presenting the training data of limited number and producing an output according to an input. Once the output function set of the fuzzy rules is obtained, it can be used in engineering fields such as automatic control and system identification so as to operate a hardware system in the way matching users' requirements.
Presently, a modeling method of a neuro-fuzzy system usually includes two phases: “structure identification” and “parameter identification.” The structure identification defines a base model, which is built by the fuzzy rules, in correspondence with the training data, and then the parameter identification adjusts parameters in the base model by a learning algorithm. Generally, the modeling result of the constructed neuro-fuzzy system with refined parameters adjusted by the parameter identification may be insufficient if the base model defined by the structure identification is not proper. Therefore, a preferable way to define a proper base model of a neuro-fuzzy system is important in a modeling method thereof.
Specifically, the structure identification is clustering-based usually. The training data is previously divided into a plurality of groups by data clustering, with each group presented by one of the fuzzy rules, and thus a neural network is built in correspondence with the fuzzy rules. Conventionally, each fuzzy rule is presented in the Takagi-Sugeno-Kang (TSK) type, and the following is the jth fuzzy rule for example.IF x1 is Aj,1 and x2 is Aj,2 and . . . and xn is Aj,n, THEN y is cj,0+cj,1x1+cj,2x2+ . . . +cj,nxn,wherein the “xi,” with “i” being a number between “1” through “n,” is the ith dimension of the input of the neuro-fuzzy system, the “y” is the output of the neuro-fuzzy system, the “Aj,i” is the membership function of the “xi” in the jth fuzzy rule, the cj,i×i is a weighted coefficient of the ith dimension of the input in the jth fuzzy rule, cj,0 is a constant, and the “n” is the total of the dimensions of the input.
However, all the weighted coefficients cj,i×i|ni=0 are usually identified by the parameter identification except for the first weighted coefficient cj,0 that is identified by the structure identification, and thus the speed to obtain the output function set is slow as well as the cost in computation of learning is large. Therefore, a new modeling method of neuro-fuzzy system is needed.